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2,041 to 2,050 of 2,228 Results
02_Prism_flow_diagram.pdf
Feb 19, 2026 - Data for “The interaction between visual and oculomotor functions and the use of virtual reality: A scoping review”
Adobe PDF - 87.9 KB - MD5: a7469fbbb03e18a9e3875f0e8ec844cf
This file contains data that visualises the study selection process from record identification in databases and citation chaining to final inclusion in the scoping review.
03_PRISMA-ScR-Checklist.pdf
Feb 19, 2026 - Data for “The interaction between visual and oculomotor functions and the use of virtual reality: A scoping review”
Adobe PDF - 105.3 KB - MD5: 053a45cd0677683178d8a72432d43e91
This file contains the PRISMA‑ScR checklist, which documents compliance with systematic review reporting standards and indicates whether the scoping review is presented in a systematic and standardised manner.
04_Data_charting_table.csv
Feb 19, 2026 - Data for “The interaction between visual and oculomotor functions and the use of virtual reality: A scoping review”
Comma Separated Values - 26.4 KB - MD5: 924731eff6134ce74b43164db6552d78
This file contains the data charting table used in the scoping review, including all extracted variables and the information charted from each study.
05_Studies_included_in_review.pdf
Feb 19, 2026 - Data for “The interaction between visual and oculomotor functions and the use of virtual reality: A scoping review”
Adobe PDF - 97.4 KB - MD5: fa6ebf69a55bc51e40bac707214d2dd2
This file contains references to all the studies included in the scoping review.
Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
Feb 24, 2026
Neville Aloysius D’Souza; Pfeiffer, Carlos; Mirlekar, Gaurav, 2026, "Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production", https://doi.org/10.18710/KIJEWJ, DataverseNO, V1
The data set consists of simulated time‑series measurements from two gas‑lifted subsea oil wells, used to develop and evaluate data‑driven virtual flow metering (VFM) models for oil and gas flow rate prediction. Purpose: To assess a range of machine learning algorithms (10 methods, including LSTM, MLP, XGBoost, SVR, tree‑based and linear methods) f...
00_ReadMe.txt
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
Plain Text - 4.1 KB - MD5: d876283a934a820f7703dd26e950e133
calculate_output.m
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
MATLAB Source Code - 459 B - MD5: bfc538b18e25ef6c92be4f828244e4a7
make_w_gc.m
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
MATLAB Source Code - 1.1 KB - MD5: 61bda33eb19fff36c06dc94044f2e65a
ML_dev_part1.ipynb
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
Jupyter Notebook - 255.9 KB - MD5: 15e085e50a122a36c8cc28635f956db7
ML_dev_part2.ipynb
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
Jupyter Notebook - 240.4 KB - MD5: aec99d2af4b73cb120944552410a6f16
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